This paper tackles the problem of optimal configuration and deployment of fault-tolerant time-critical service chains with arbitrary DAG-alike topologies. We propose RTilience, designed according to a scalable cloud microservice paradigm, and prototyped on top of the well-known Kubernetes cloud orchestrator. It features real-time reservation scheduling of containers to guarantee temporal isolation of time-critical tasks, leading to fine-grained control of compute latencies, while allowing for sharing physical CPUs among containers. A distributed routing library, ReqRoute, is configured with a timeout and primary and secondary routes, enabling autonomous and decentralized handling of failing requests. The routes are configured by a centralized controller that performs admission control, resource management of microservice instances, task placement, and fault detection and recovery, extending the features available in Kubernetes. Admission control is based on a theoretical framework enclosing a worst-case performance model for the experienced end-to-end response-time under various fault handling options, and an optimization framework that computes the optimum resource allocation for admitted services. Extensive experimentation of the proposed solution has been performed with synthetic examples, and an autonomous transport robot use-case, verifying that end-to-end deadlines are effectively respected, even in presence of high fault rates of individual microservice instances, according to the theoretical expectations. RTilience is made available as open-source software, released under a MIT license.

Summary: In this work, we present eMolTox, a web server for the prediction of potential toxicity associated with a given molecule. A total of 174 toxicology-related in vitro/vivo experimental datasets were used for model construction and Mondrian conformal prediction was used to estimate the confidence of the resulting predictions. Toxic substructure analysis is also implemented in eMolTox. eMolTox predicts and displays a wealth of information of potential molecular toxicities for safety analysis in drug development. Availability and implementation: The eMolTox Server is freely available for use on the web at http://xundrug.cn/moltox. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: In silico approaches often fail to utilize bioactivity data available for orthologous targets due to insufficient evidence highlighting the benefit for such an approach. Deeper investigation into orthologue chemical space and its influence toward expanding compound and target coverage is necessary to improve the confidence in this practice. Results: Here we present analysis of the orthologue chemical space in ChEMBL and PubChem and its impact on target prediction. We highlight the number of conflicting bioactivities between human and orthologues is low and annotations are overall compatible. Chemical space analysis shows orthologues are chemically dissimilar to human with high intra-group similarity, suggesting they could effectively extend the chemical space modelled. Based on these observations, we show the benefit of orthologue inclusion in terms of novel target coverage. We also benchmarked predictive models using a time-series split and also using bioactivities from Chemistry Connect and HTS data available at AstraZeneca, showing that orthologue bioactivity inclusion statistically improved performance. Availability and implementation: Orthologue-based bioactivity prediction and the compound training set are available at www.github.com/lhm30/PIDGINv2. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Calvin is an IoT framework for application development, deployment and execution in heterogeneous environments, that includes clouds, edge resources, and embedded or constrained resources. Inside Calvin, all the distributed resources are viewed as one environment by the application. The framework provides multi-tenancy and simplifies development of IoT applications, which are represented using a dataflow of application components (named actors) and their communication. The idea behind Calvin poses similarity with the serverless architecture and can be seen as Actor as a Service instead of Function as a Service. This makes Calvin very powerful as it does not only scale actors quickly but also provides an easy actor migration capability. In this work, we propose Calvin Constrained, an extension to the Calvin framework to cover resource-constrained devices. Due to limited memory and processing power of embedded devices, the constrained side of the framework can only support a limited subset of the Calvin features. The current implementation of Calvin Constrained supports actors implemented in C as well as Python, where the support for Python actors is enabled by using MicroPython as a statically allocated library, by this we enable the automatic management of state variables and enhance code re-usability. As would be expected, Python-coded actors demand more resources over C-coded ones. We show that the extra resources needed are manageable on current off-the-shelve micro-controller-equipped devices when using the Calvin framework.

We are concerned with the problem of detecting an overtaking vehicle using a single camera mounted behind the ego-vehicle windscreen. The proposed solution makes use of 1D optical flow evaluated along lines parallel to the motion of the overtaking vehicles. The 1D optical flow is computed by tracking features along these lines. Based on these features, the position of the overtaking vehicle can also be estimated. The proposed solution has been implemented and tested in real time with promising results. The video data was recorded during test drives in normal traffic conditions in Sweden and Germany.